{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node classification with Graph ATtention Network (GAT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/gat-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/gat-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import NetworkX and stellar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GAT\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the CORA network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, node_subjects = dataset.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5429\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5429]\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    node_subjects, train_size=140, test_size=None, stratify=node_subjects\n",
    ")\n",
    "val_subjects, test_subjects = model_selection.train_test_split(\n",
    "    test_subjects, train_size=500, test_size=None, stratify=test_subjects\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Rule_Learning': 9,\n",
       "         'Neural_Networks': 42,\n",
       "         'Case_Based': 16,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Reinforcement_Learning': 11,\n",
       "         'Theory': 18})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_subjects)\n",
    "val_targets = target_encoding.transform(val_subjects)\n",
    "test_targets = target_encoding.transform(test_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GAT model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GAT is a full-batch model, we use the `FullBatchNodeGenerator` class to feed node features and graph adjacency matrix to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = FullBatchNodeGenerator(G, method=\"gat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_subjects.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GAT layers with 8-dimensional hidden node features for the first layer and the 7 class classification output for the second layer.\n",
    " * `attn_heads` is the number of attention heads in all but the last GAT layer in the model\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * Arguments such as `bias`, `in_dropout`, `attn_dropout` are internal parameters of the model, execute `?GAT` for details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To follow the GAT model architecture used for Cora dataset in the original paper [Graph Attention Networks. P. Veličković et al. ICLR 2018 https://arxiv.org/abs/1710.10903], let's build a 2-layer GAT model, with the second layer being the classifier that predicts paper subject: it thus should have the output size of `train_targets.shape[1]` (7 subjects) and a softmax activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gat = GAT(\n",
    "    layer_sizes=[8, train_targets.shape[1]],\n",
    "    activations=[\"elu\", \"softmax\"],\n",
    "    attn_heads=8,\n",
    "    generator=generator,\n",
    "    in_dropout=0.5,\n",
    "    attn_dropout=0.5,\n",
    "    normalize=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expose the input and output tensors of the GAT model for node prediction, via GAT.in_out_tensors() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, predictions = gat.in_out_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `predictions` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_subjects.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create callbacks for early stopping (if validation accuracy stops improving) and best model checkpoint saving:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "if not os.path.isdir(\"logs\"):\n",
    "    os.makedirs(\"logs\")\n",
    "es_callback = EarlyStopping(\n",
    "    monitor=\"val_acc\", patience=20\n",
    ")  # patience is the number of epochs to wait before early stopping in case of no further improvement\n",
    "mc_callback = ModelCheckpoint(\n",
    "    \"logs/best_model.h5\", monitor=\"val_acc\", save_best_only=True, save_weights_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1/1 - 0s - loss: 2.0089 - acc: 0.1286 - val_loss: 1.8361 - val_acc: 0.3440\n",
      "Epoch 2/50\n",
      "1/1 - 0s - loss: 1.8543 - acc: 0.2786 - val_loss: 1.7295 - val_acc: 0.3860\n",
      "Epoch 3/50\n",
      "1/1 - 0s - loss: 1.7033 - acc: 0.4143 - val_loss: 1.6395 - val_acc: 0.3880\n",
      "Epoch 4/50\n",
      "1/1 - 0s - loss: 1.5959 - acc: 0.4500 - val_loss: 1.5602 - val_acc: 0.3960\n",
      "Epoch 5/50\n",
      "1/1 - 0s - loss: 1.4899 - acc: 0.4786 - val_loss: 1.4882 - val_acc: 0.4360\n",
      "Epoch 6/50\n",
      "1/1 - 0s - loss: 1.4368 - acc: 0.5286 - val_loss: 1.4199 - val_acc: 0.5040\n",
      "Epoch 7/50\n",
      "1/1 - 0s - loss: 1.3643 - acc: 0.4786 - val_loss: 1.3548 - val_acc: 0.5840\n",
      "Epoch 8/50\n",
      "1/1 - 0s - loss: 1.1994 - acc: 0.6286 - val_loss: 1.2938 - val_acc: 0.6420\n",
      "Epoch 9/50\n",
      "1/1 - 0s - loss: 1.2092 - acc: 0.6357 - val_loss: 1.2381 - val_acc: 0.6880\n",
      "Epoch 10/50\n",
      "1/1 - 0s - loss: 1.1780 - acc: 0.6214 - val_loss: 1.1883 - val_acc: 0.7460\n",
      "Epoch 11/50\n",
      "1/1 - 0s - loss: 1.0629 - acc: 0.7571 - val_loss: 1.1438 - val_acc: 0.7540\n",
      "Epoch 12/50\n",
      "1/1 - 0s - loss: 0.9646 - acc: 0.7786 - val_loss: 1.1008 - val_acc: 0.7660\n",
      "Epoch 13/50\n",
      "1/1 - 0s - loss: 0.9697 - acc: 0.7357 - val_loss: 1.0610 - val_acc: 0.7660\n",
      "Epoch 14/50\n",
      "1/1 - 0s - loss: 0.9924 - acc: 0.7571 - val_loss: 1.0250 - val_acc: 0.7760\n",
      "Epoch 15/50\n",
      "1/1 - 0s - loss: 0.9264 - acc: 0.7929 - val_loss: 0.9903 - val_acc: 0.7820\n",
      "Epoch 16/50\n",
      "1/1 - 0s - loss: 0.9189 - acc: 0.7571 - val_loss: 0.9571 - val_acc: 0.7900\n",
      "Epoch 17/50\n",
      "1/1 - 0s - loss: 0.7993 - acc: 0.8429 - val_loss: 0.9268 - val_acc: 0.7960\n",
      "Epoch 18/50\n",
      "1/1 - 0s - loss: 0.7887 - acc: 0.8429 - val_loss: 0.8978 - val_acc: 0.8060\n",
      "Epoch 19/50\n",
      "1/1 - 0s - loss: 0.7488 - acc: 0.8714 - val_loss: 0.8694 - val_acc: 0.8120\n",
      "Epoch 20/50\n",
      "1/1 - 0s - loss: 0.8564 - acc: 0.7929 - val_loss: 0.8415 - val_acc: 0.8140\n",
      "Epoch 21/50\n",
      "1/1 - 0s - loss: 0.6635 - acc: 0.8643 - val_loss: 0.8154 - val_acc: 0.8120\n",
      "Epoch 22/50\n",
      "1/1 - 0s - loss: 0.7209 - acc: 0.8571 - val_loss: 0.7931 - val_acc: 0.8160\n",
      "Epoch 23/50\n",
      "1/1 - 0s - loss: 0.7705 - acc: 0.7714 - val_loss: 0.7732 - val_acc: 0.8240\n",
      "Epoch 24/50\n",
      "1/1 - 0s - loss: 0.7272 - acc: 0.7857 - val_loss: 0.7560 - val_acc: 0.8280\n",
      "Epoch 25/50\n",
      "1/1 - 0s - loss: 0.5554 - acc: 0.8643 - val_loss: 0.7410 - val_acc: 0.8320\n",
      "Epoch 26/50\n",
      "1/1 - 0s - loss: 0.6467 - acc: 0.7929 - val_loss: 0.7278 - val_acc: 0.8340\n",
      "Epoch 27/50\n",
      "1/1 - 0s - loss: 0.5838 - acc: 0.9000 - val_loss: 0.7157 - val_acc: 0.8340\n",
      "Epoch 28/50\n",
      "1/1 - 0s - loss: 0.5867 - acc: 0.8786 - val_loss: 0.7062 - val_acc: 0.8340\n",
      "Epoch 29/50\n",
      "1/1 - 0s - loss: 0.5223 - acc: 0.8500 - val_loss: 0.6971 - val_acc: 0.8360\n",
      "Epoch 30/50\n",
      "1/1 - 0s - loss: 0.6319 - acc: 0.8214 - val_loss: 0.6904 - val_acc: 0.8340\n",
      "Epoch 31/50\n",
      "1/1 - 0s - loss: 0.5651 - acc: 0.8429 - val_loss: 0.6837 - val_acc: 0.8340\n",
      "Epoch 32/50\n",
      "1/1 - 0s - loss: 0.5362 - acc: 0.8429 - val_loss: 0.6789 - val_acc: 0.8280\n",
      "Epoch 33/50\n",
      "1/1 - 0s - loss: 0.5855 - acc: 0.8357 - val_loss: 0.6736 - val_acc: 0.8260\n",
      "Epoch 34/50\n",
      "1/1 - 0s - loss: 0.5116 - acc: 0.8929 - val_loss: 0.6671 - val_acc: 0.8280\n",
      "Epoch 35/50\n",
      "1/1 - 0s - loss: 0.6374 - acc: 0.8000 - val_loss: 0.6612 - val_acc: 0.8280\n",
      "Epoch 36/50\n",
      "1/1 - 0s - loss: 0.4589 - acc: 0.8714 - val_loss: 0.6546 - val_acc: 0.8300\n",
      "Epoch 37/50\n",
      "1/1 - 0s - loss: 0.5649 - acc: 0.8286 - val_loss: 0.6479 - val_acc: 0.8300\n",
      "Epoch 38/50\n",
      "1/1 - 0s - loss: 0.4521 - acc: 0.8786 - val_loss: 0.6407 - val_acc: 0.8280\n",
      "Epoch 39/50\n",
      "1/1 - 0s - loss: 0.5769 - acc: 0.8429 - val_loss: 0.6335 - val_acc: 0.8320\n",
      "Epoch 40/50\n",
      "1/1 - 0s - loss: 0.6275 - acc: 0.8071 - val_loss: 0.6255 - val_acc: 0.8320\n",
      "Epoch 41/50\n",
      "1/1 - 0s - loss: 0.5329 - acc: 0.8286 - val_loss: 0.6178 - val_acc: 0.8300\n",
      "Epoch 42/50\n",
      "1/1 - 0s - loss: 0.5978 - acc: 0.8286 - val_loss: 0.6110 - val_acc: 0.8280\n",
      "Epoch 43/50\n",
      "1/1 - 0s - loss: 0.4852 - acc: 0.8857 - val_loss: 0.6047 - val_acc: 0.8280\n",
      "Epoch 44/50\n",
      "1/1 - 0s - loss: 0.5016 - acc: 0.8714 - val_loss: 0.5996 - val_acc: 0.8280\n",
      "Epoch 45/50\n",
      "1/1 - 0s - loss: 0.4713 - acc: 0.9000 - val_loss: 0.5963 - val_acc: 0.8320\n",
      "Epoch 46/50\n",
      "1/1 - 0s - loss: 0.4520 - acc: 0.8643 - val_loss: 0.5945 - val_acc: 0.8340\n",
      "Epoch 47/50\n",
      "1/1 - 0s - loss: 0.4885 - acc: 0.8786 - val_loss: 0.5934 - val_acc: 0.8340\n",
      "Epoch 48/50\n",
      "1/1 - 0s - loss: 0.4595 - acc: 0.8786 - val_loss: 0.5932 - val_acc: 0.8300\n",
      "Epoch 49/50\n",
      "1/1 - 0s - loss: 0.4557 - acc: 0.8571 - val_loss: 0.5929 - val_acc: 0.8300\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    epochs=50,\n",
    "    validation_data=val_gen,\n",
    "    verbose=2,\n",
    "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
    "    callbacks=[es_callback, mc_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the saved weights of the best model found during the training (according to validation accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"logs/best_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_subjects.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.7227\n",
      "\tacc: 0.8109\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_subjects.index\n",
    "all_gen = generator.flow(all_nodes)\n",
    "all_predictions = model.predict(all_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specification to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for full-batch methods the batch size is 1 and the predictions have shape $(1, N_{nodes}, N_{classes})$ so we we remove the batch dimension to obtain predictions of shape $(N_{nodes}, N_{classes})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few predictions after training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31336</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1061127</td>\n",
       "      <td>Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1106406</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13195</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37879</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1126012</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1107140</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1102850</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31349</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1106418</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1123188</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1128990</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109323</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217139</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31353</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32083</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1126029</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1118017</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49482</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>753265</td>\n",
       "      <td>Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Predicted                    True\n",
       "31336           Neural_Networks         Neural_Networks\n",
       "1061127           Rule_Learning           Rule_Learning\n",
       "1106406  Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140  Reinforcement_Learning                  Theory\n",
       "1102850         Neural_Networks         Neural_Networks\n",
       "31349           Neural_Networks         Neural_Networks\n",
       "1106418                  Theory                  Theory\n",
       "1123188         Neural_Networks         Neural_Networks\n",
       "1128990         Neural_Networks      Genetic_Algorithms\n",
       "109323          Neural_Networks   Probabilistic_Methods\n",
       "217139   Reinforcement_Learning              Case_Based\n",
       "31353           Neural_Networks         Neural_Networks\n",
       "32083           Neural_Networks         Neural_Networks\n",
       "1126029  Reinforcement_Learning  Reinforcement_Learning\n",
       "1118017         Neural_Networks         Neural_Networks\n",
       "49482           Neural_Networks         Neural_Networks\n",
       "753265          Neural_Networks         Neural_Networks"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Predicted\": node_predictions, \"True\": node_subjects})\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of the 1st GraphAttention layer in GAT layer stack (the one before the top classification layer predicting paper subjects), and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node embedding space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. We find the embedding layer by taking the first graph attention layer in the stack of Keras layers. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding layer: graph_attention_sparse, output shape (1, 2708, 64)\n"
     ]
    }
   ],
   "source": [
    "emb_layer = next(l for l in model.layers if l.name.startswith(\"graph_attention\"))\n",
    "print(\n",
    "    \"Embedding layer: {}, output shape {}\".format(emb_layer.name, emb_layer.output_shape)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=emb_layer.output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The embeddings can now be calculated using the predict function. Note that the embeddings returned are 64 dimensional features (8 dimensions for each of the 8 attention heads) for all nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2708, 64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict(all_gen)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their true subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the embeddings from the GAT model have a batch dimension of 1 so we `squeeze` this to get a matrix of $N_{nodes} \\times N_{emb}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb.squeeze()\n",
    "y = np.argmax(target_encoding.transform(node_subjects), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=list(G.nodes()))\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GAT embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/gat-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/gat-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
